Prediction of fungicidal activities of rice blast disease based on least-squares support vector machines and project pursuit regression.
Three machine learning methods, genetic algorithm-multilinear regression (GA-MLR), least-squares support vector machine (LS-SVM), and project pursuit regression (PPR), were used to investigate the relationship between thiazoline derivatives and their fungicidal activities against the rice blast disease. The GA-MLR method was used to select the most appropriate molecular descriptors from a large set of descriptors, which were only calculated from molecular structures, and develop a linear quantitative structure-activity relationship (QSAR) model at the same time. On the basis of the selected descriptors, the other two more accurate models (LS-SVM and PPR) were built. Both the linear and nonlinear modes gave good prediction results, but the nonlinear models afforded better prediction ability, which meant that the LS-SVM and PPR methods could simulate the relationship between the structural descriptors and fungicidal activities more accurately. The results show that the nonlinear methods (LS-SVM and PPR) could be used as good modeling tools for the study of rice blast. Moreover, this study provides a new and simple but efficient approach, which should facilitate the design and development of new compounds to resist the rice blast disease.
Du
Hongying
H
Department of Chemistry, Lanzhou University, Lanzhou, China.
Wang
Jie
J
Hu
Zhide
Z
Yao
Xiaojun
X
Zhang
Xiaoyun
X
D000465:Algorithms; D001185:Artificial Intelligence; D015195:Drug Design; D005659:Fungicides, Industrial / Q000737:chemistry* / Q000494:pharmacology; D016018:Least-Squares Analysis; D016014:Linear Models; D020082:Magnaporthe / Q000187:drug effects; D012275:Oryza / Q000382:microbiology*; D010935:Plant Diseases / Q000382:microbiology*; D021281:Quantitative Structure-Activity Relationship; D013844:Thiazoles / Q000737:chemistry* / Q000494:pharmacology
D005659:Fungicides, Industrial; D013844:Thiazoles